Keywords
participatory modelling, random forest, Best Practices
Start Date
16-9-2020 4:40 PM
End Date
16-9-2020 5:00 PM
Abstract
The participatory modeling field has grown quickly and new applications are continuing to be developed. A recent review of participatory modeling case studies has identified that various forms of the practice exist (e.g., different ways to identify participants, define problems, include values, manage power dynamics, etc.), and that practices can generate very important positive outcomes such as knowledge integration, learning, and uncertainty management. However, results found that not all approaches were successful in achieving objectives. The uncertainty in successful practices may discourage practitioners from using this approach and limits future advances in participatory modeling. Therefore, knowing which characteristics are associated with positive outcomes can inform future practices and increase utilization. To address this gap, we collected data from 157 participatory modeling case studies in the peer-reviewed literature and used a random forest model to identify attributes associated with positive outcomes. We present results from this analysis and share insights in how these findings can inform planning and use of different types of participatory processes.
Identifying Participatory Modeling Best Practices through Statistical Review of Case Studies
The participatory modeling field has grown quickly and new applications are continuing to be developed. A recent review of participatory modeling case studies has identified that various forms of the practice exist (e.g., different ways to identify participants, define problems, include values, manage power dynamics, etc.), and that practices can generate very important positive outcomes such as knowledge integration, learning, and uncertainty management. However, results found that not all approaches were successful in achieving objectives. The uncertainty in successful practices may discourage practitioners from using this approach and limits future advances in participatory modeling. Therefore, knowing which characteristics are associated with positive outcomes can inform future practices and increase utilization. To address this gap, we collected data from 157 participatory modeling case studies in the peer-reviewed literature and used a random forest model to identify attributes associated with positive outcomes. We present results from this analysis and share insights in how these findings can inform planning and use of different types of participatory processes.
Stream and Session
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